Client application workloads for data systems are known to vary in load and access patterns over time. This variability can place undue stress on data systems, tying up resources and degrading performance. To meet this challenge, systems must adapt by adjusting resource allocation and processing techniques to ameliorate contention and to deliver stable performance. We demonstrate Dendrite, a system designed to bootstrap adaptivity for data systems through its widely-applicable approach for extracting metrics, developing adaption rules, and applying them through user-defined functions to effect system behaviour changes. We highlight Dendrite's features and capabilities through a proof-of-concept implementation with the popular PostgreSQL database system.

Indexes for multidimensional data based on the R-Tree are popularly used by databases for a wide range of applications. Such index trees support point and range queries but are costly to construct over datasets of millions of points. We present the Non-Intersecting R-Tree (NIR-Tree), a novel insert-efficient, in-memory, multidimensional index that uses bounding polygons to provide efficient point and range query performance while indexing data at least an order of magnitude faster. The NIR-Tree leverages non-intersecting bounding polygons to reduce the number of nodes accessed during queries, compared to existing R-family indexes. Our experiments demonstrate that inserting into a NIR-Tree is 27 × faster than the ubiquitous R*-Tree, with point queries completing 2 × faster and range queries executing just as quickly.

Single-master replicated database systems strive to be scalable by offloading reads to replica nodes. However, single-master systems suffer from the performance bottleneck of all updates executing at a single site. Multi-master replicated systems distribute updates among sites but incur costly coordination for multi-site transactions. We present DynaMast, a lazily replicated, multi-master database system that guarantees one-site transaction execution while effectively distributing both reads and updates among multiple sites. DynaMast benefits from these advantages by dynamically transferring the mastership of data, or remastering, among sites using a lightweight metadata-based protocol. DynaMast leverages remastering to adaptively place master copies to balance load and minimize future remastering. Using benchmark workloads, we demonstrate that DynaMast delivers superior performance over existing replicated database system architectures.

The complexity of modern data systems and applications greatly increases the challenge in understanding system behaviour and diagnosing performance problems. When these problems arise, system administrators are left with the difficult task of remedying them by relying on large debug log files, vast numbers of metrics, and system-specific tooling. We demonstrate the Sentinel system, which enables administrators to analyze systems and applications by building models of system execution and comparing them to derive key differences in behaviour. The resulting analyses are then presented as system reports to administrators and developers in an intuitive fashion. Users of Sentinel can locate, identify and take steps to resolve the reported performance issues. As Sentinel's models are constructed online by intercepting debug logging library calls, Sentinel's functionality incurs little overhead and works with all systems that use standard debug logging libraries.

The performance of data-driven, web-scale client applications is sensitive to access latency. To address this concern, enterprises strive to cache data on edge nodes that are closer to users, thereby avoiding expensive round-trips to remote data centers. However, these geo-distributed approaches are limited to caching static data. In this paper we present ChronoCache, a mid-tier caching system that exploits the presence of geo-distributed edge nodes to cache database query results closer to users. ChronoCache transparently learns and leverages client application access patterns to predictively combine query requests and cache their results ahead of time, thereby reducing costly round-trips to the remote database. We show that ChronoCache reduces query response times by up to 2/3 over prior approaches on multiple representative benchmark workloads. representative benchmark workloads.

Distributed database systems are widely used to meet the demands of storing and managing computation-heavy workloads. To boost performance and minimize resource and data contention, these systems require selecting a distributed physical design that determines where to place data, and which data items to replicate and partition. Deciding on a physical design is difficult as each choice poses a trade-off in the design space, and a poor choice can significantly degrade performance. Current design decisions are typically static and cannot adapt to workload changes or are unable to combine multiple design choices such as data replication and data partitioning integrally. This paper presents MorphoSys , a distributed database system that dynamically chooses, and alters, its physical design based on the workload. MorphoSys makes integrated design decisions for all of the data partitioning, replication and placement decisions on-the-fly using a learned cost model. MorphoSys provides efficient transaction execution in the face of design changes via a novel concurrency control and update propagation scheme. Our experimental evaluation, using several benchmark workloads and state-of-the-art comparison systems, shows that MorphoSys delivers excellent system performance through effective and efficient physical designs.

The ubiquity of distributed computing systems has led to an increased focus on distributed systems in the undergraduate curriculum. In this paper, we describe the design of, and our experiences with, a new distributed systems project where students implement the core components of a distributed file system called WatDFS. The WatDFS project enables students to meet learning objectives from across the distributed systems curriculum and interact with real systems, while providing a high-quality testing environment that yields actionable feedback to students on their submissions.

Cloud storage systems typically choose between replicating or erasure encoding data to provide fault tolerance. Replication ensures that data can be accessed from a single site but incurs a much higher storage overhead, which is a costly downside for large-scale storage systems. Erasure coding has a lower storage requirement but relies on encoding/decoding and distributed data retrieval, which can result in straggling requests that increase response times. We propose strategies for data access and data movement within erasure-coded storage systems that significantly reduce data retrieval times. We present EC-Store, a system that incorporates these dynamic strategies for data access and movement based on workload access patterns. Through detailed evaluation using two benchmark workloads, we show that EC-Store incurs significantly less storage overhead than replication while achieving better performance than both replicated and erasure-coded storage systems.